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        <title>How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control Techniques</title>
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    <abstract>Recent advances in the development of large Pretrained Language Models, such as GPT, BERT and Bloom, have achieved remarkable performance on a wide range of different NLP tasks. However, when used for text generation tasks, these models still have limitations when it comes to controlling the content and style of the generated text, often producing content that is incorrect, irrelevant, or inappropriate in the context of a given task. In this survey paper, we explore methods for controllable text generation with a focus on sentiment control. We systematically collect papers from the ACL Anthology, create a categorisation scheme based on different control techniques and controlled attributes, and use the scheme to categorise and compare methods. The result is a detailed and comprehensive overview of state-of-the-art techniques for sentiment-controlled text generation categorised on the basis of how the control is implemented and what attributes are controlled and providing a clear idea of their relative strengths and weaknesses.</abstract>
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%0 Conference Proceedings
%T How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control Techniques
%A Lorandi, Michela
%A Belz, Anya
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lorandi-belz-2023-control
%X Recent advances in the development of large Pretrained Language Models, such as GPT, BERT and Bloom, have achieved remarkable performance on a wide range of different NLP tasks. However, when used for text generation tasks, these models still have limitations when it comes to controlling the content and style of the generated text, often producing content that is incorrect, irrelevant, or inappropriate in the context of a given task. In this survey paper, we explore methods for controllable text generation with a focus on sentiment control. We systematically collect papers from the ACL Anthology, create a categorisation scheme based on different control techniques and controlled attributes, and use the scheme to categorise and compare methods. The result is a detailed and comprehensive overview of state-of-the-art techniques for sentiment-controlled text generation categorised on the basis of how the control is implemented and what attributes are controlled and providing a clear idea of their relative strengths and weaknesses.
%R 10.18653/v1/2023.wassa-1.30
%U https://aclanthology.org/2023.wassa-1.30
%U https://doi.org/10.18653/v1/2023.wassa-1.30
%P 341-353

Markdown (Informal)

[How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control Techniques](https://aclanthology.org/2023.wassa-1.30) (Lorandi & Belz, WASSA 2023)

• How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control Techniques (Lorandi & Belz, WASSA 2023)

ACL

• Michela Lorandi and Anya Belz. 2023. How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control Techniques. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 341–353, Toronto, Canada. Association for Computational Linguistics.